Matúš Žilinec


2021

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Backtranslation Feedback Improves User Confidence in MT, Not Quality
Vilém Zouhar | Michal Novák | Matúš Žilinec | Ondřej Bojar | Mateo Obregón | Robin L. Hill | Frédéric Blain | Marina Fomicheva | Lucia Specia | Lisa Yankovskaya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.

2020

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ELITR Non-Native Speech Translation at IWSLT 2020
Dominik Macháček | Jonáš Kratochvíl | Sangeet Sagar | Matúš Žilinec | Ondřej Bojar | Thai-Son Nguyen | Felix Schneider | Philip Williams | Yuekun Yao
Proceedings of the 17th International Conference on Spoken Language Translation

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-to-end general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set.